More and more information is available on the Web, and the current search engines do a great job to make it accessible. Yet, optimizing for a large number of users, they usually provide good answers only to “most of us", and have yet to provide satisfying mechanisms to search for audiovisual content.

In this talk I will present ongoing work at L3S addressing these challenges. I will start by giving a brief overview of Web Science areas covered at L3S, and the main challenges we adress in these areas, with the Web of People as one important focal point of our research, as well as Web Information Management and Web Search.

In the second part of the talk, I will discuss search for audiovisual content, and how to make this content more accessible. As many of our algorithms focus on exploiting user generated information, I will discuss what kinds of tags are used for different resources and how they can help for search. Collaborative tagging has become an increasingly popular means for sharing and organizing Web resources, leading to a
huge amount of user generated metadata. These tags represent different aspects of the resources they describe and it is not obvious whether and how these tags or subsets of them can be used for search. I will present an in-depth study of tagging behavior for different kinds of resources - Web pages, music, and images. I will also discuss how to enrich existing tags through machine learning methods, to provide indexing more appropriate to user search behavior.

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